In the recent explosion of interest in deep RL, “model-free” approaches based on Q-learning and actor-critic architectures have received the most attention due to their flexibility and ease of use. However, this generality often comes at the expense of efficiency (statistical as well as computational) and robustness. The large number of required samples and safety concerns often limit direct use of model-free RL for real-world settings.
Model-based methods are expected to be more efficient. Given accurate models, trajectory optimization and Monte-Carlo planning methods can efficiently compute near-optimal actions in varied contexts. Advances in generative modeling, unsupervised, and self-supervised learning provide methods for learning models and representations that support subsequent planning and reasoning. Against this backdrop, our workshop aims to bring together researchers in generative modeling and model-based control to discuss research questions at their intersection, and to advance the state of the art in model-based RL for robotics and AI. In particular, this workshops aims to make progress on questions related to:
May 3: Paper deadline
(23:59 hours AOE time)
May 20: Notifications
June 14: Workshop
We invite the submission of short papers, up to 4 pages (excluding references and supplementary material). Submissions should be anonymous and in the ICML 2019 format (see official style guidelines). All accepted submissions will be made available on the workshop website and included in the poster session during the workshop (this does not constitute archival publication). Relevant topics include but are not limited to:
Generative Modeling
Model-Based Planning and Control
Interplay between model learning and model-based control
We also highly welcome and encourage scientific position papers under the workshop theme.
Please submit your manuscripts here.
Contact: mbrl.workshop.icml19@gmail.com